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FNSPID: A Comprehensive Financial News Dataset in Time Series (2402.06698v1)

Published 9 Feb 2024 in q-fin.ST

Abstract: Financial market predictions utilize historical data to anticipate future stock prices and market trends. Traditionally, these predictions have focused on the statistical analysis of quantitative factors, such as stock prices, trading volumes, inflation rates, and changes in industrial production. Recent advancements in LLMs motivate the integrated financial analysis of both sentiment data, particularly market news, and numerical factors. Nonetheless, this methodology frequently encounters constraints due to the paucity of extensive datasets that amalgamate both quantitative and qualitative sentiment analyses. To address this challenge, we introduce a large-scale financial dataset, namely, Financial News and Stock Price Integration Dataset (FNSPID). It comprises 29.7 million stock prices and 15.7 million time-aligned financial news records for 4,775 S&P500 companies, covering the period from 1999 to 2023, sourced from 4 stock market news websites. We demonstrate that FNSPID excels existing stock market datasets in scale and diversity while uniquely incorporating sentiment information. Through financial analysis experiments on FNSPID, we propose: (1) the dataset's size and quality significantly boost market prediction accuracy; (2) adding sentiment scores modestly enhances performance on the transformer-based model; (3) a reproducible procedure that can update the dataset. Completed work, code, documentation, and examples are available at github.com/Zdong104/FNSPID. FNSPID offers unprecedented opportunities for the financial research community to advance predictive modeling and analysis.

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Citations (4)
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Summary

  • The paper introduces FNSPID, a dataset that integrates 29.7 million stock prices with 15.7 million financial news articles for improved market predictions.
  • It employs web scraping and text summarization techniques including LexRank and LSA, enhanced by ChatGPT-generated sentiment labels.
  • Experimental results show transformer models achieving an R² of 0.988, demonstrating the dataset's efficacy for multimodal financial forecasting.

FNSPID: A Comprehensive Financial News Dataset in Time Series

The paper "FNSPID: A Comprehensive Financial News Dataset in Time Series" (2402.06698) introduces an extensive dataset that integrates financial news sentiment and stock prices to enhance the prediction of market dynamics. This essay reviews the dataset's construction, properties, evaluation, and potential uses while addressing the implications for machine learning in financial markets.

Introduction to FNSPID

FNSPID, the Financial News and Stock Price Integration Dataset, was developed to address the paucity of comprehensive datasets that incorporate both sentiment data from financial news and quantitative stock price analyses. The dataset boasts 29.7 million stock prices and 15.7 million aligned financial news articles for S&P 500 companies, spanning from 1999 to 2023. It amalgamates diverse sources such as Yahoo Finance, Bloomberg, and Reuters, focusing on time-aligned data crucial for sequence-to-sequence prediction models. Figure 1

Figure 1: Data Collection Process from website selection in the first level box; data segmentation in second level boxes; data collection for web scraping on left and numerical data collection on right; data organization on fourth level boxes and final FNSPID build-up on the last level box.

Data Collection and Processing

The dataset constructs its extensive corpus using web scraping techniques from trusted financial sources, adhering to ethical data use standards and focusing on sustaining data integrity and diversity (Figure 1). It summarizes news articles using algorithms such as LexRank and LSA to manage token limitations for sentiment analysis. Sentiment scores are further enhanced by ChatGPT-generated labels, reflecting an integration of LLMs for nuanced financial analysis. These processes ensure that the dataset is comprehensive, combining the strengths of machine-readable formats and semantic analysis. Figure 2

Figure 2: Example ChatGPT Prompt: The first section is the system prompt, defining constraints and specifying the task for ChatGPT. In the second section, two examples are included to guide ChatGPT on the desired content for the response. Subsequently, the summarized news is fed into ChatGPT for sentiment score labeling.

Properties and Evaluation

FNSPID is notable for its scale and diversity. It aligns sentiment data with financial metrics, offering a versatile tool for predictive modeling. Experiments using the dataset demonstrate its utility: transformer models trained on FNSPID yielded superior prediction accuracy (R2=0.988R^2 = 0.988) compared to traditional models (R2=0.856R^2 = 0.856 for LSTM). This underscores the advantage of incorporating sentiment analysis with quantitative data in enhancing financial forecasts. Figure 3

Figure 3: Sentiment Data: Where 'Symbol' represents the stock code (e.g., AAPL for Apple Inc.); 'LSM Sum', 'Luhn Sum', 'TextRank Sum', and 'LexRank Sum' encapsulate the summarized news information generated by three different algorithms.

Application and Ethics

FNSPID presents multiple applications, such as training multimodal models that leverage textual and numerical data for robust stock market predictions and refining sentiment analysis within financial contexts. The dataset serves as a benchmark for developing AI-driven advisory tools in the financial sector.

Ethically, the dataset emphasizes privacy, compliance with data protection laws, and responsible model deployment. It also addresses potential biases in predictive analytics, ensuring fairness and transparency in financial modeling.

Conclusion

FNSPID stands as a significant advancement in financial forecasting resources, offering substantial potential for enhancing machine learning applications in finance. Its integration of sentiment and numerical data enables detailed analysis and accurate predictions, reinforcing the efficacy of AI in financial research. Future developments may focus on expanding data sources and refining sentiment analysis techniques to further improve prediction models. The dataset's ethical considerations set a precedent for responsible use in financial analytics, promoting trust and transparency in AI applications.

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Practical Applications

Immediate Applications

Below are practical applications that can be deployed now, based directly on the dataset, findings, and methods presented in the paper.

  • Financial modeling benchmarks and baselines (Academia, Finance)
    • Use FNSPID to benchmark time-series and multimodal models (LSTM, GRU, Transformer, TimesNet) against numerical-only baselines and sentiment-enhanced inputs.
    • Workflow: ingest aligned price-news sequences; train/validate models using the paper’s splits and feature sets; compare MAE/MSE/R² across architectures.
    • Assumptions/dependencies: access to the 30+GB dataset; standard DL toolchain (PyTorch/TensorFlow); reproducible scripts from the provided GitHub; awareness that transformer models benefit more from larger data.
  • Sentiment-augmented trading signals and risk dashboards (Finance)
    • Add the 1–5 sentiment factor and the exponential sentiment decay to daily models for short-horizon signals, risk alerts, and volatility forecasting.
    • Workflow: compute daily sentiment features (with decay and averaging for multiple articles); integrate with price/volume features; backtest on 1999–2023 coverage.
    • Assumptions/dependencies: sentiment labels exist only for a subset (402K items); labeling quality matters (ChatGPT > TextBlob in this study); model hyperparameters require tuning; moderate to high compute resources.
  • Event studies and anomaly detection (Finance, Academia)
    • Identify abnormal returns around high-sentiment or high-volume news events, spot pre-crisis news patterns (e.g., 2008–2009), and detect sentiment-price divergences.
    • Workflow: tag events by sentiment spikes; run abnormal return/event-window analyses; design anomaly detectors for news-pattern precursors.
    • Assumptions/dependencies: accurate time alignment; controls for confounders; publication delays may dilute immediate impact.
  • Factor research and portfolio construction with sentiment (Finance)
    • Construct a “sentiment factor” to augment standard factors (e.g., Fama-French) for cross-sectional return models or portfolio tilts.
    • Workflow: build daily firm-level sentiment scores; test factor exposures; incorporate into portfolio optimizer; monitor Sharpe/turnover.
    • Assumptions/dependencies: stationarity challenges; sentiment may be more impactful in certain sectors; transaction costs may erode improvements.
  • Model selection heuristics for practitioners (Finance, Academia)
    • Apply empirical guidance: transformers outperform others at scale; LSTM/GRU are competitive on smaller samples; sentiment helps transformers modestly.
    • Workflow: choose architectures based on data availability; add sentiment for transformer pipelines; for smaller datasets, prefer LSTM/GRU.
    • Assumptions/dependencies: performance contingent on hyperparameters; sentiment preprocessing quality; coverage bias.
  • Multilingual sentiment and coverage analysis (Academia)
    • Use English/Russian splits to study cross-lingual market impact and global news coverage patterns.
    • Workflow: perform language-stratified analyses; test model robustness across languages; build language-specific sentiment features.
    • Assumptions/dependencies: varying article density by language; translation quality if needed; domain-specific lexicon differences.
  • RL strategy prototyping with aligned multimodal data (Finance, Software)
    • Train reinforcement learning agents with synchronized price-volume-sentiment states for daily decision-making.
    • Workflow: adapt FinRL/FinGPT pipelines; include sentiment as state feature; evaluate reward structures (PnL, risk-adjusted returns).
    • Assumptions/dependencies: RL stability and overfitting risks; careful reward design; realistic transaction/slippage modeling.
  • Retail investor tools: sentiment overlays and digest (Daily life, Fintech)
    • Build watchlist apps that summarize and score daily news per ticker using provided summarization (LSA/LexRank/TextRank) and sentiment scales.
    • Workflow: daily fetch from FNSPID; display sentiment trends and summaries; alert on significant sentiment shifts.
    • Assumptions/dependencies: coverage throttled by licensing and scraping constraints; avoid overreliance on short-term sentiment for long-term decisions.
  • Corporate communications and IR monitoring (Industry)
    • Monitor how external news sentiment correlates with stock movements for internal PR/IR strategy and crisis response.
    • Workflow: sentiment trend dashboards; correlation heatmaps; post-event attribution.
    • Assumptions/dependencies: sentiment scores reflect public narratives but not internal fundamentals; lag between news release and market reaction.
  • Market integrity surveillance (Policy, Regulators)
    • Detect potential manipulation by analyzing abnormal sentiment clusters followed by unusual price-volume patterns.
    • Workflow: rules/ML for sentiment-price anomalies; flag investigations; link to enforcement workflows.
    • Assumptions/dependencies: need domain thresholds; separating legitimate narratives from manipulation; governance over automated flags.

Long-Term Applications

These opportunities likely require further research, scaling, engineering, or policy development before broad deployment.

  • Real-time, scalable multimodal pipelines (Finance, Software)
    • Build streaming ingestion that continuously aligns live news with tick-level/daily prices and produces on-the-fly sentiment features.
    • Potential products: “Live Sentiment Factor” feeds; real-time dashboards for traders.
    • Assumptions/dependencies: robust, licensed news feeds; API reliability; low-latency summarization/sentiment models; cost control.
  • Domain-specific financial LLMs that natively handle numbers (Finance, AI)
    • Fine-tune LLMs (e.g., FinGPT variants) on FNSPID to jointly reason over text and time series for forecasting, risk commentary, and decision support.
    • Potential tools: multimodal transformer architectures; retrieval-augmented generation for financial contexts.
    • Assumptions/dependencies: high compute; careful training to avoid hallucinations; numeric reasoning fidelity; updated datasets.
  • Advanced RL for portfolio and execution using market news (Finance)
    • Integrate market microstructure, risk constraints, and sentiment-aware state spaces to optimize dynamic allocation and trade execution.
    • Potential workflows: hierarchical RL; multi-agent simulation; risk-aware reward shaping.
    • Assumptions/dependencies: synthetic market environments; robust generalization; compliance with trading regulations.
  • Cross-lingual and cross-market expansion (Global finance, Academia)
    • Extend to more languages, asset classes (bonds, commodities, crypto), and regions; enable comparative studies of news regimes.
    • Potential products: global sentiment indices; sectoral impact maps (e.g., healthcare, energy, tech).
    • Assumptions/dependencies: localized news sources; diverse compliance requirements; translation/normalization accuracy.
  • Better sentiment labeling via domain-tuned models and human-in-the-loop (Academia, Industry)
    • Replace generic labeling with finance-specific sentiment models; add human review for edge cases and high-stakes events.
    • Potential tools: weak supervision, active learning, confidence scoring; event-type classifiers (earnings, M&A, litigation, regulation).
    • Assumptions/dependencies: labeling budget; improved ontologies; avoiding bias; maintaining temporal consistency.
  • Policy analytics: stress testing and macroprudential early warning (Policy)
    • Use aggregated sentiment indices to stress test sectors/markets and inform macroprudential decisions (e.g., circuit breakers, disclosure timing).
    • Potential workflows: sentiment shock scenarios; systemic risk dashboards; supervisory analytics.
    • Assumptions/dependencies: validated link from sentiment to macro outcomes; careful communication to avoid procyclicality; governance for public use.
  • Compliance, ESG, and reputational risk measurement (Policy, Finance)
    • Track ESG-related news sentiment per issuer; quantify reputational risk and assess materiality for disclosure and compliance.
    • Potential tools: ESG sentiment scoring; topic attribution; regulatory reporting integrations.
    • Assumptions/dependencies: reliable topic classification; evolving ESG taxonomies; heterogeneous regulatory standards.
  • Education and curriculum integration (Education)
    • Develop hands-on courses and competitions around FNSPID for students to learn time-series ML, sentiment analysis, and financial modeling.
    • Potential products: course modules, Kaggle-style challenges, model labs.
    • Assumptions/dependencies: managed compute for classrooms; simplified subsets of data; ethical training on scraping and data use.
  • Investor decision support assistants (Daily life, Fintech)
    • Create consumer-grade AI advisors that explain news impact on portfolios, provide scenario analysis, and synthesize multi-source narratives.
    • Potential tools: explainable AI components; guardrails; preference modeling.
    • Assumptions/dependencies: strict compliance (suitability, disclaimers); reliable personalization; prevention of overconfidence.
  • Market manipulation detection standards (Policy, Industry)
    • Collaborate to define standards for sentiment-price anomaly detection, audit trails, and transparent explainability in financial AI.
    • Potential products: shared benchmarks; regulatory sandboxes; audit toolkits.
    • Assumptions/dependencies: multi-stakeholder cooperation; privacy safeguards; periodic model audits.

General Assumptions and Dependencies Across Applications

  • Data licensing and ethics: scraping must respect robots.txt, copyrights, and regional policies; using third-party APIs (e.g., ChatGPT) entails cost and compliance.
  • Coverage and bias: dataset drawn from selected sources (e.g., NASDAQ, Reuters, Bloomberg, Benzinga, Lenta) may reflect source biases; survivorship bias and symbol coverage should be checked.
  • Sentiment quality: summarization (Sumy LSA/LexRank/TextRank/Luhn) reduces tokens but may lose nuance; ChatGPT-labeled scores outperform TextBlob here but still have variability; decay model assumes diminishing impact toward neutral.
  • Model sensitivity: transformer gains are modest from sentiment and larger with more data; hyperparameter tuning materially affects outcomes; high accuracy ceilings limit incremental gains.
  • Compute and MLOps: training at scale needs GPU resources and disciplined pipelines; real-time deployments need robust engineering.
  • Temporal alignment: news timing vs. market reaction lag must be modeled; batching multiple articles per day via averaging can mask asymmetric impacts.
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